LLMOps Course

6 production systems — each with deployable infra artifacts you present in interviews and ship at work. 01 Multi-Model Inference Gateway Unified API with latency SLAs, concurrency limits, and fallback routing. Load test report (p95/p99 at representative concurrency — e.g., 500 concurrent users) Grafana dashboard: throughput, error rate, GPU utilization Canary rollout config with eval gate and auto-rollback 02 RAG Pipeline with Eval Harness Retrieval-augmented generation with continuous evaluation — not a one-off demo. Ragas faithfulness + relevancy scores with acceptance thresholds LangSmith trace dashboard: retriever latency, chunk hit-rate CI gate: golden-set regression blocks deploy if recall drops beyond an agreed threshold (e.g., 5%) 03 Fine-Tuning Ops Pipeline LoRA/QLoRA adapter to m

Every module is designed around what actually breaks in production — and how to prevent, detect, and recover from it. Master LLM Deployment at Scale Deploy models with vLLM and DeepSpeed across GPU clusters — continuous batching, canary rollouts, and automatic rollback on eval gate failure. PromptOps & Evaluation Pipelines Version, trace, and regression-test prompts with LangSmith. Golden-set pass rate is evaluated against an agreed benchmark (for example, 92%+) before promoting a prompt version. Quantization & Fine-Tuning LoRA/QLoRA adapters to merged production models with eval gates. Quantization tradeoff matrix: INT4 vs INT8 vs FP16 on latency, accuracy, and VRAM. LangChain & LangServe in Production Structured LLM deployment with per-step timeouts, circuit breakers, streaming error rec

LLMOps (Large Language Model Operations) is the discipline of deploying, monitoring, and scaling production LLM systems. It covers model serving, evaluation gates, prompt and adapter versioning, observability, security guardrails, and cost control. LLMOps vs MLOps (Engineering Comparison) Area MLOps LLMOps Primary workload Training + batch/online inference for ML models Real-time LLM APIs with token streaming and tool calls Serving & latency Model servers, feature stores, predictable payloads Inference engines (vLLM/TGI/Triton), batching, KV-cache, p95/p99 under load Quality control Offline metrics, data drift, model monitoring Golden-set eval gates, prompt regressions, RAG retrieval quality (Ragas/Promptfoo) Versioning Datasets + model versions Prompts, adapters (LoRA/QLoRA), chains/agent

Most failures aren't about prompts — they're operational: serving bottlenecks, missing eval gates, weak observability, and uncontrolled cost. This program teaches the failure modes and the infrastructure patterns to prevent, detect, and recover. Latency spikes & queueing collapse Burst traffic, KV-cache pressure, batching misconfig, cold starts, or upstream dependency failures. Silent quality regressions Prompt edits, adapter updates, or RAG changes ship without golden-set regression testing and acceptance gates. Observability blind spots No traces for tool calls, no cost-per-request visibility, and no drift/hallucination alerting. Security & data leakage Prompt injection, weak authN/authZ, missing tool allowlists, and inadequate audit logging. RAG retrieval mismatch Stale embeddings, brok

Six operational pillars — each taught through hands-on projects with measurable infrastructure outcomes, not slides.

No marketing fluff — here is exactly how we back up every claim on this page.

Live instruction, async reviews, and always-on support — designed so working engineers don't have to pause their day jobs to level up.

Three tracks, one goal — production-ready AI. Pick the depth that matches where you are. MLOps End-to-end ML pipelines Model versioning & CI/CD Docker + K8s for ML MLflow & feature stores Explore MLOps YOU ARE HERE LLMOps LLM deployment & operations vLLM, LangServe, TGI serving LangSmith & Langfuse tracing Quantization & cost control AIOps MLOps + LLMOps + AgentOps combined Full-stack AI infrastructure RAG pipelines & PromptOps Agent deployment & governance Explore AIOps